In applications such as character recognition, some classes are heavily overlapped but are not necessarily to be separated. For classification of such overlapping classes, either discriminating between them or merging...
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ISBN:
(纸本)9783540699385
In applications such as character recognition, some classes are heavily overlapped but are not necessarily to be separated. For classification of such overlapping classes, either discriminating between them or merging them into a metaclass does not satisfy. Merging the overlapping classes into a metaclass implies that with in- metaclass substitution is considered as correct classification. For such classification problems, I propose a partial discriminative training (PDT) scheme for neuralnetworks, in which, a training pattern of an overlapping class is used as a positive sample of its labeled class, and neither positive nor negative sample for its allied classes (classes overlapping with the labeled class). In experiments of handwritten letter recognition using neuralnetworks and support vector machines, the PDT scheme mostly outperforms crosstraining (a scheme for multi-labeled classification), ordinary discriminative training and metaclass classification.
This paper investigates the use of artificialneuralnetworks (ANN) to mine and predict;patterns in software aging phenomenon. We analyze resource usage data collected on a typical long-running software system: a web ...
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ISBN:
(纸本)9783540699385
This paper investigates the use of artificialneuralnetworks (ANN) to mine and predict;patterns in software aging phenomenon. We analyze resource usage data collected on a typical long-running software system: a web server. A Multi-Layer Perceptron feed forward artificialneural Network was trained on an Apache web server dataset to predict future server swap space and physical free memory resource exhaustion through ANN univariate tirne series forecasting and ANN nonlinear multivari ' ate time series empirical modeling. The results were benchmarked against those obtained from non-parametric statistical techniques, parametric time series models and other empirical modeling techniques reported in the literature.
This paper presents a novel neural network model, called similarity neural network (SNN), designed to learn similarity measures for pairs of patterns. The model guarantees to compute a non negative and symmetric measu...
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ISBN:
(纸本)9783540699385
This paper presents a novel neural network model, called similarity neural network (SNN), designed to learn similarity measures for pairs of patterns. The model guarantees to compute a non negative and symmetric measure, and shows good generalization capabilities even if a very small set of supervised examples is used for training. Preliminary experiments, carried out on some UCI datasets, are presented, showing promising results.
In this paper we propose two new ensemble combiners based on the Mixture of neuralnetworks model. In our experiments, we have applied two different network architectures on the methods based on the Mixture of neural ...
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ISBN:
(纸本)9783540699385
In this paper we propose two new ensemble combiners based on the Mixture of neuralnetworks model. In our experiments, we have applied two different network architectures on the methods based on the Mixture of neuralnetworks: the Basic Network (BN) and the Multilayer Feedforward Network (MF). Moreover, we have used ensembles of MF networks previously trained with Simple Ensemble to test the performance of the combiners we propose. Finally, we compare the mixture combiners proposed with three different mixture models and other traditional combiners. The results show that the mixture combiners proposed are the best way to build Multi-net systems among the methods studied in the paper in general.
We have implemented a speech command system which can understand simple command sentences like "Bot lift ball" or "Bot go table" using hidden Markov models (HMMs) and associative memories with spar...
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ISBN:
(纸本)9783540699385
We have implemented a speech command system which can understand simple command sentences like "Bot lift ball" or "Bot go table" using hidden Markov models (HMMs) and associative memories with sparse distributed representations. The system is composed of three modules: (1) A set of HMMs is used on phoneme level to get a phonetic transcription of the spoken sentence, (2) a network of associative memories is used to determine the word belonging to the phonetic transcription and (3) a neural network is used on the sentence level to determine the meaning of the sentence. The system is also able to learn new object words during performance.
Among the approaches to build a Multi-Net system, Stacked Generalization is a well-known model. The classification system is divided into two steps. Firstly, the level-O generalizers are built using the original input...
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ISBN:
(纸本)9783540699385
Among the approaches to build a Multi-Net system, Stacked Generalization is a well-known model. The classification system is divided into two steps. Firstly, the level-O generalizers are built using the original input data and the class label. Secondly, the level-] generalizers networks are built using the outputs of the level-O generalizers and the class label. Then, the model is ready for patternrecognition. We have found two important adaptations of Stacked Generalization that can be applyied to artificialneuralnetworks. Moreover, two combination methods, Stacked and Stacked+, based on the Stacked Generalization idea were successfully introduced by our research group. In this paper, we want to empirically compare the version of the original Stacked Generalization along with other traditional methodologies to build Multi-Net systems. Moreover, we have also compared the combiners we proposed. The best results are provided by the combiners Stacked and Stacked+ when they are applied to ensembles previously trained with Simple Ensemble.
Prototype based classifiers so far can only work with hard labels on the training data. In order to allow for soft labels as input label and answer, we enhanced the original LVQ algorithm. The key idea is adapting the...
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ISBN:
(纸本)9783540699385
Prototype based classifiers so far can only work with hard labels on the training data. In order to allow for soft labels as input label and answer, we enhanced the original LVQ algorithm. The key idea is adapting the prototypes depending on the similarity of their fuzzy labels to the ones of training samples. In experiments, the performance of the fuzzy LVQ was compared against the original approach. Of special interest was the behaviour of the two approaches, once noise was added to the training labels, and here a clear advantage of fuzzy versus hard training labels could be shown.
To win a board-game or more generally to gain something specific in a given Markov-environment, it is most important to have a policy in cboosing and taking actions that leads to one of several qualitative good states...
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ISBN:
(纸本)9783540699385
To win a board-game or more generally to gain something specific in a given Markov-environment, it is most important to have a policy in cboosing and taking actions that leads to one of several qualitative good states. In this paper we describe a novel method to learn a game-winning strategy. The method predicts statistical probabilities to win in given game states using a state-value function that is approximated by a Multi-layer perceptron. Those predictions will improve according to rewards given in terminal states. We have deployed that method in the game Connect Four and have compared its gameperformance with Velena [5].
One of the most frequently used models for classification tasks is the Probabilistic neural Network. Several improvements of the Probabilistic neural Network have been proposed such as the Evolutionary Probabilistic N...
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ISBN:
(纸本)9783540699385
One of the most frequently used models for classification tasks is the Probabilistic neural Network. Several improvements of the Probabilistic neural Network have been proposed such as the Evolutionary Probabilistic neural Network that employs the Particle Swarm Optimization stochastic algorithm for the proper selection of its spread (smoothing) parameters and the prior probabilities. To further improve its performance, a fuzzy class membership function has been incorporated for the weighting of its pattern layer neurons. For each neuron of the pattern layer, a fuzzy class membership weight is computed and it is multiplied to its output in order to magnify or decrease the neuron's signal when applicable. Moreover, a novel scheme for multi-class problems is proposed since the fuzzy membership function can be incorporated only in binary classification tasks. The proposed model is entitled Fuzzy Evolutionary Probabilistic neural Network and is applied to several realworld benchmark problem with promising results.
Recursive Feature Elimination RFE combined with feature-ranking is an effective technique for eliminating irrelevant features. In this paper, an ensemble of MLP base classifiers with feature-ranking based on the magni...
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ISBN:
(纸本)9783540699385
Recursive Feature Elimination RFE combined with feature-ranking is an effective technique for eliminating irrelevant features. In this paper, an ensemble of MLP base classifiers with feature-ranking based on the magnitude of MLP weights is proposed. This approach is compared experimentally with other popular feature-ranking methods, and with a Support Vector Classifier SVC. Experimental results on natural benchmark data and on a problem in facial action unit classification demonstrate that the MLP ensemble is relatively insensitive to the feature-ranking method, and simple ranking methods perform as well as more sophisticated schemes. The results are interpreted with the assistance of bias/variance of 0/1 loss function.
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